1. Field of the Invention
This invention relates to a method for verifying, validating and improving vehicle fault models that includes performing a what-if analysis using experts to identify significant failure modes and symptoms using field failure data, learning simulation parameters from the field failure data, simulating faults using the learned parameters, generating simulations using the what-if analysis and the fault model along with diagnostic reasoner to provide estimated faults and comparing the estimated faults to the simulated faults for benefit analysis.
2. Discussion of the Related Art
Modern vehicles are complex electrical and mechanical systems that employ many components, devices, modules, sub-systems, etc. that pass electrical information between and among each other using sophisticated algorithms and data buses. As with anything, these types of devices and algorithms are susceptible to errors, failures and faults that affect the operation of the vehicle. When such errors and faults occur, often the affected device or component will issue a fault code, such as diagnostic trouble code (DTC), that is received by one or more system controller identifying the fault, or some ancillary fault with an integrated component. These DTCs can be analyzed by service technicians and engineers to identify problems and/or make system corrections and upgrades. However, given the complexity of vehicle systems, many DTCs and other signals could be issued for many different reasons, which could make trouble-shooting particular difficult.
Vehicle fault models that define the faults that could occur in a vehicle and the remedies available for those faults are becoming more prevalent in the industry. One of the most simplistic representations of a fault model is a two-dimensional matrix where the rows of the matrix capture the failure modes that could occur on the vehicle and the columns of the matrix identify the symptoms that the vehicle may experience for the failure modes. The fault model captures the causal dependencies among the failure modes and symptoms. The various symptoms could be information that is recorded during operation of the vehicle, or information that is occurring while the vehicle is being serviced. Thus, by placing an indicator at the cross section between a particular failure mode and the symptoms that the vehicle would undergo for those failure modes in the fault model, service personnel can identify what service operation needs to be performed based on the symptoms that are occurring to correct a particular failure.
Depending on the scope of the fault model, the matrix may be very large, and may be updated and refined so that it is eventually able to identify specific repair operations for each possible symptom. Further, various fault models can be provided for different levels of the vehicle, where such fault models can be provided for specific vehicle subsystems, fault models can be provided for specific vehicle brands, makes, model, etc.
It is desirable to accurately populate fault models so that they do not employ redundant information, they accurately identify the failures and they accurately identify the symptoms related to those failures. In other words, it is desirable to have a methodology to verify and validate integrated vehicle health management (IVHM) fault models by a systematic methodology linked to field failure data collected from many vehicles.